Journal article
Machine Learning-Assisted Identification and Quantification of Hydroxylated Metabolites of Polychlorinated Biphenyls in Animal Samples
Environmental Science & Technology, Vol.56(18), pp.13169-13178
09/01/2022
DOI: 10.1021/acs.est.2c02027
PMCID: PMC9573770
PMID: 36047920
Appears in UI Libraries Support Open Access
Abstract
Laboratory studies of the disposition and toxicity of hydroxylated polychlorinated biphenyl (OH-PCB) metabolites are challenging because authentic analytical standards for most unknown OH-PCBs are not available. To assist with the characterization of these OH-PCBs (as methylated derivatives), we developed machine learning-based models with multiple linear regression (MLR) or random forest regression (RFR) to predict the relative retention times (RRT) and MS/MS responses of methoxylated (MeO-)PCBs on a gas chromatograph-tandem mass spectrometry system. The final MLR model estimated the retention times of MeO-PCBs with a mean absolute error of 0.55 min (n = 121). The similarity coefficients cos θ between the predicted (by RFR model) and experimental MS/MS data of MeO-PCBs were >0.95 for 92% of observations (n = 96). The levels of MeO-PCBs quantified with the predicted MS/MS response factors approximated the experimental values within a 2-fold difference for 85% of observations and 3-fold differences for all observations (n = 89). Subsequently, these model predictions were used to assist with the identification of OH-PCB 95 or OH-PCB 28 metabolites in mouse feces or liver by suggesting candidate ranking information for identifying the metabolite isomers. Thus, predicted retention and MS/MS response data can assist in identifying unknown OH-PCBs.
Details
- Title: Subtitle
- Machine Learning-Assisted Identification and Quantification of Hydroxylated Metabolites of Polychlorinated Biphenyls in Animal Samples
- Creators
- Chun-Yun Zhang - University of Iowa, Occupational and Environmental HealthKimberly P Keil Stietz - University of California, DavisSunjay Sethi - University of California, DavisWeizhu Yang - University of ArizonaRachel F Marek - University of Iowa, IIHR--Hydroscience and EngineeringXinxin Ding - University of ArizonaPamela J Lein - University of California, DavisKeri C Hornbuckle - University of Iowa, IIHR--Hydroscience and EngineeringHans-Joachim Lehmler - University of Iowa, Occupational and Environmental Health
- Resource Type
- Journal article
- Publication Details
- Environmental Science & Technology, Vol.56(18), pp.13169-13178
- DOI
- 10.1021/acs.est.2c02027
- PMID
- 36047920
- PMCID
- PMC9573770
- NLM abbreviation
- Environ Sci Technol
- ISSN
- 0013-936X
- eISSN
- 1520-5851
- Publisher
- American Chemical Society
- Grant note
- DOI: 10.13039/100000066, name: National Institute of Environmental Health Sciences, award: ES005605, ES013661, ES014901, ES027169, ES031098
- Language
- English
- Date published
- 09/01/2022
- Academic Unit
- Civil and Environmental Engineering; Occupational and Environmental Health; Iowa Neuroscience Institute; IIHR--Hydroscience and Engineering; Interdisciplinary Graduate Program in Human Toxicology; Iowa Superfund Research Program
- Record Identifier
- 9984293959202771
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